Combining clustering and abundances of galaxy clusters to test cosmology and primordial non-Gaussianity
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We present the clustering of galaxy clusters as a useful addition to the common set of cosmological observables. The clustering of clusters probes the large-scale structure of the Universe, extending galaxy clustering analysis to the high-peak, high-bias regime. Clustering of galaxy clusters complements the traditional cluster number counts and observable–mass relation analyses, significantly improving their constraining power by breaking existing calibration degeneracies. We use the maxBCG galaxy clusters catalogue to constrain cosmological parameters and cross-calibrate the mass–observable relation, using cluster abundances in richness bins and weak-lensing mass estimates. We then add the redshift-space power spectrum of the sample, including an effective modelling of the weakly non-linear contribution and allowing for an arbitrary photometric redshift smoothing. The inclusion of the power spectrum data allows for an improved self-calibration of the scaling relation. We find that the inclusion of the power spectrum typically brings a ∼50 per cent improvement in the errors on the fluctuation amplitude σ8 and the matter density Ωm. Finally, we apply this method to constrain models of the early universe through the amount of primordial non-Gaussianity of the local type, using both the variation in the halo mass function and the variation in the cluster bias. We find a constraint on the amount of skewness fNL = 12 ± 157 (1σ) from the cluster data alone.
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Clustering properties of these galaxy clusters will be used to constrain cosmological parameters, marginalize over mass-observable relation and photometric redshift uncertainties, and constrain supersample covariance in WL and LSS two-point functions.
“The clustering properties of those same galaxy clusters will also be used to constrain cosmological parameters ( Mo et al. 1996; Mana et al. 2013 ) , to marginalize over uncertainties in the mass -observable relation and photometric redshift uncertainties ( Oguri & Takada 2011 ) , and to constrain the effects of supersample covariance in the two-point functions of WL and LSS ( Hu & Kravts”
From LSST: From Science Drivers to Reference Design and Anticipated Data Products (2019) · cites this paper as Mana et al. 2013
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- Authors
- Mana, A., Giannantonio, T., Weller, J., Hoyle, B., Hutsi, G., & Sartoris, B.
- Affiliations
- Excellence Cluster Universe (DE)
- Max Planck Institute for Extraterrestrial Physics (DE)
- University of Trieste (IT)
- Venue
- Monthly Notices of the Royal Astronomical Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-07-12
- Volume
- 434
- Issue
- 1
- Pages
- 684-695
- ISSN
- 0035-8711 (ISSN-L)
- 1365-2966
- OpenAlex
- W2156604644
- Cited by
- 66
- References
- 107
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 5 · 2023: 6 · 2022: 1 · 2021: 7 · 2020: 5 · 2019: 6 · 2018: 3 · 2017: 4 · 2016: 9 · 2015: 5 · 2014: 9 · 2013: 2
- FWCI
- 4.41
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Monthly Notices of the Royal Astronomical Society (publishedVersion)
- arXiv (Cornell University) (publishedVersion)
- ArTS Archivio della ricerca di Trieste (University of Trieste https://www.units.it/) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Deutsche Forschungsgemeinschaft (TRR 33)
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Cosmology and Gravitation Theories
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
- Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › Dark Matter and Cosmic Phenomena
- Keywords
- cluster abundance, cosmological parameters, primordial non-Gaussianity, local non-Gaussianity, f_NL, cluster number counts, mass-observable relation, redshift-space power spectrum, photometric redshifts, halo mass function, cluster bias, sigma_8, matter density, large-scale structure
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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